Scalable Homology Classification through Decomposed Euler Characteristic Curves
Summary
A scalable classification framework using decomposed Euler characteristic curves as efficient topological descriptors for machine learning and pattern recognition tasks.
Research Context
This publication reflects graduate research conducted at the University of Cincinnati College of Engineering and Applied Science. It is included here to document the technical foundation behind Convergent Analytics' work in industrial analytics, applied AI, high-performance computing, and topological data analysis.
This peer-reviewed conference publication was produced as part of graduate research conducted at the University of Cincinnati College of Engineering and Applied Science.
The paper presents a classification framework based on decomposed Euler characteristic curves, enabling efficient representation of topological information for machine learning workflows. The approach provides a computationally practical alternative to more expensive topological descriptors.
The research demonstrates how topological summaries can be incorporated into predictive analytics pipelines while maintaining scalability for larger datasets and real-world analytical applications.
Citation
Malott, N.O., Wilsey, P.A. Scalable Homology Classification through Decomposed Euler Characteristic Curves. IEEE Big Data 2023.